In this paper, we investigate a large intelligent surface-enhanced (LIS-enhanced) system, where a LIS is deployed to assist secure transmission. Our design aims to maximize the achievable secrecy rates in different channel models, i.e., Rician fading, and (or) independent, and identically distributed Gaussian fading for the legitimate, and eavesdropper channels. In addition, we take into consideration an artificial noise-aided transmission structure for further improving system performance. The difficulties of tackling the aforementioned problems are the structure of the expected secrecy rate expressions, and the non-convex phase shift constraint. To facilitate the design, we propose two frameworks, namely the sample average approximation based (SAA-based) algorithm, and the hybrid stochastic projected gradient-convergent policy (hybrid SPG-CP) algorithm, to calculate the expectation terms in the secrecy rate expressions. Meanwhile, majorization minimization (MM) is adopted to address the non-convexity of the phase shift constraint. In addition, we give some analyses on two special scenarios by making full use of the expectation terms. Simulation results show that the proposed algorithms effectively optimize the secrecy communication rate for the considered setup, and the LIS-enhanced system greatly improves secrecy performance compared to conventional architectures without LIS.

中文翻译：

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大型智能表面辅助物理层安全传输

在本文中，我们研究了一个大型智能表面增强（LIS 增强）系统，其中部署了 LIS 以协助安全传输。我们的设计旨在最大限度地提高不同信道模型中可实现的保密率，即，赖斯衰落和（或）独立的、同分布的合法和窃听信道的高斯衰落。此外，我们还考虑了人工噪声辅助传输结构，以进一步提高系统性能。解决上述问题的难点在于期望保密率表达式的结构和非凸相移约束。为了便于设计，我们提出了两个框架，即基于样本平均近似（SAA-based）的算法，和混合随机投影梯度收敛策略（混合 SPG-CP）算法，计算保密率表达式中的期望项。同时，采用多数化最小化（MM）来解决相移约束的非凸性。此外，我们还充分利用了期望项，对两种特殊情况进行了分析。仿真结果表明，所提出的算法有效地优化了所考虑设置的保密通信速率，与没有 LIS 的传统架构相比，LIS 增强系统大大提高了保密性能。我们充分利用期望项对两种特殊情况进行了分析。仿真结果表明，所提出的算法有效地优化了所考虑设置的保密通信速率，与没有 LIS 的传统架构相比，LIS 增强系统大大提高了保密性能。我们充分利用期望项对两种特殊情况进行了分析。仿真结果表明，所提出的算法有效地优化了所考虑设置的保密通信速率，与没有 LIS 的传统架构相比，LIS 增强系统大大提高了保密性能。